Text feature selection based on improved adaptive GA

Text feature selection is crucial to improve the speed and accuracy of text categorization, and then improves the effect of complex network information filtering. Considering feature selection as an optimization problem, an improved genetic algorithm (GA) is proposed to fulfill text feature selection in this paper. Traditional adaptive genetic algorithm has poor adaptation. Compared with the traditional adaptive genetic algorithm, the fitness function formula, crossover probability formula and mutation probability formula are improved. We illustrated the validity of the improved genetic algorithm through experiments.